Metamaterials
lenovo
2023-05-09
stimuliresponsive materials will be able to
react to the stimuli of external physical
fields.
When stimulated, the metamaterials can
automatically deform, make motions, and
change their structural properties or
functions according to external
environments,
the changes of the material
microstructures when the external field
(such as, temperature, external force)
continuously applies to the material until a
specific condition
Strain mismatch refers to the discontinuous changes of strain
Due to the different mechanical properties, the uncoordinated strain
of each part results in
internal stress at the interface under the effect of environmental or
load conditions, which furtherly lead to the bending or deformation
of the structure.
material-instability-based metamaterial
design
is to controlling the number of the
minimum potential energy
points of the materials
unconstrained homogeneous materials
generally yield uniformly expansions or
contractions as the temperature rises or
falls
data-driven methods--unlabeled data to
search for undetected patterns of the
given dataset
rewarding desired behaviors and/or
punishing undesired ones
rewarding desired behaviors and/or
punishing undesired ones
map inputs to outputs with data being
labeled
establish the relation of the input/output
parameters
match the input data structure required
by the selected ML model, and consist of
essential material
features to ensure high accuracy and
training efficiency
Gradient-based algorithms require
gradient or sensitivity information, in
addition to function evaluations, to
determine adequate search directions for
better designs during optimization
iterations.
mechanism: properties to create reconfidurable/
Topics Metamaterials
Additive M
ML
Lab Available
Material Jetting
DESIGN
Recommd
[3] Machine learning-based inverse design of auxetic metamaterial with zero
Poisson's ratio
PROPERTY
[4] Inverse Design of Inflatable Soft Membranes Through
Machine Learning
INVERSE DESIGN
Origami
Kirigami
REVIEW
APPLICATION
REVIEW: stimuli-responsive materials
external physical fields
heat/Temperature
chemicals
light field
electric current
magnetic field
pressure action
SOFT ROBOT
Microfluidics
Flexible energy storage materials
WEARABLE devices/sensors
Bionic gripper.
Paper collection
phase transition
Driving force
strain mismatch
mechanical instability
topology optimization
solid phase, liquid phase, and gas phase
Important Reviews
Optimization
materials deployment=>active
deformation and controllable response
Equipment
Others
Simulation software Available
Material Available
Binder Jetting
a powder based material and a binder
Material Extrusion
Powder Bed Fusion(PBF)
Sheet Lamination
Directed Energy Deposition(DED)
VAT Photopolymerisation
liquid photopolymer resin+light
Fuse deposition modelling (FDM)
Direct metal laser sintering (DMLS), Electron beam melting (EBM), Selective heat sintering
(SHS), Selective laser melting (SLM) and Selective laser sintering (SLS)
PATTERN
1D,2D->3D
different expansion coefficients material
stimulated by a external (force or thermal)
stimuli
unconstrained homogeneous materials
Material
Stimuli
1D,2D->3D->4D
different expansion coefficients material
stimulated by a external (force or thermal)
stimuli
unconstrained homogeneous materials
Material
Stimuli
Micro
Macro
structural instabilities
instabilities in microstructured materials
Scale
multi-stable structures
material-instability-based metamaterial
design
is to controlling the number of the
minimum potential energy
points of the materials
phase transformations
domain patterning
strain localization
STRUCTURAL CURVING
BUCKLING
TWISTING
WRINCLING
FOLDING
PRESSURE/INDENTATION
material instability
structural-instability
skip the uniform deformation and
rapidly jump to the position with low
potential energy,
Application:Structural Optimization
size optimization
shape optimization
express various mechanics indexes of the structure as a function related to the
material distribution, and establish optimization algorithms with constraints to find
the optimal solution, and optimize a specific performance of the material.
Application
method
arranging the distribution of the materials to obtain the desired performance of
the structure within the specified design domain
Analysis Process
Thermal-Responsive
commercially available/self-assembled 3D printer
with different printing methods (i.e., DLP, SLA, FDM, and PolyJet 3D printers in the labs for polymer and composite 3D printing. Have access to metal printing too.
stereolithography (SLA)
Digital Light Processing (DLP)
Chemical-Responsive
Light-Responsive
Electro-Responsive
Magneto-Responsive
Pressure-Responsive
Pneumatic Actuation
Hydraulic Actuation
Pros and Cons
Functions
PRACTICAL
Active Shape-Shifting
Load Bearing and Impact Protection
Elastic Waves Propagation Adjustment
Acoustic Stealth
Mobility
[5] The shell microstructure of the pteropod Creseis acicula is composed of
nested arrays of S-shaped aragonite fibers: A unique biological material
ML
Problem solving
Optimization
Design
Prediction
Methods
Supervised learning
unsupervised learning
Reinforcement learning
Methods
k-means clustering
enerative adversarial networks (GANs)
semi-supervised learning
graph neural networks (GNNs)
Methods
Methods
graph neural networks (GNNs)
graph neural networks (GNNs)
graph neural networks (GNNs)
Methods
Support vector machine(SVR)
Linear regression/polynomial regression
random forest (RF)
Feedforward neural network (FFNN); MLP
convolutional neural networks(CNNs)
Recurrent neural network
(RNN); LSTM; GRU
Generative adversarial networks (GANs)
Methods
Gaussian process regression (GPR);
Bayesian learning
Capture features at different hierarchical levels by calculating convolutions;
operate on pixel-based or voxel-based data
Charact.
structural topology optimization1
Applica.
Prediction of strain fields or elastic properties of high-contrast composites
Prediction of strain fields or elastic properties of high-contrast composites,
modulus of unidirectional
composites,stress fields in cantilevered structures, or yield
strength of additive-manufactured metals;
prediction of fatigue crack propagation in polycrystalline alloys;
prediction of crystal plasticity;
design of tessellate composites;
design of stretchable graphene kirigami;
Connect nodes (neurons) forming a
directed
graph with history information stored in
hidden states; operate on sequential data
Prediction of fracture patterns in
crystalline solids; prediction
of plastic behaviors in
heterogeneous materials;multi-scale
modeling of porous media
Train two opponent neural networks to
generate and discriminate separately until
the two networks reach equilibrium;
generate new data according to the
distribution of training set
Prediction of modulus distribution by solving inverse elasticity
problems;
prediction of strain or stress fields in composites;
composite design;
structural topology optimization;
architected materials design
Treat parameters as random variables and
calculate the probability distribution of these
variables;
quantify the uncertainty of model predictions
Modulus or strength prediction;
design of supercompressible and recoverable
metamaterials
Operate on non-Euclidean data
structures;
applicable tasks include link prediction,
node classification and graph
classification
Hardness prediction;127 architected
materials design168
Sub
Conditional Generative Adversarial Network
(CGAN)
Appli.
A generative adversarial network (GAN) is
a type of deep learning network that can
generate data with similar characteristics
as the input training data.
Defi.
generator+discriminator: [inverse design]
It could reversely predict multiple sets of metamaterial
structures that can meet the needs by inputting the required
target prop.
Appli.
membrane inflation+binary material( shape changing capabilities)
pre-programmed 3D shapes starting from 2D planar composite membranes
Analysis Process
Modeling/simulation
AM
Experimental and numerical validation
Summary:
Selection: Proper algorithm model
Inputs
outputs
types of materials
architectures(micro)
Material Property
stiffness
Database generation:
Machine learning
prediction
Model training
model evaluation
FEA
Inputs/outpus
data(2)
Application
output:generating structural property form
flexibility
Dataset
predict the properties
tailor the micro-architectures
for metamaterials according to external
conditions.
literature/existing databases
high-throughput experiments
FEA-SIMULATION
Data resource
Data preprocess
Computational problems
Model Design
1)ML-based Applicable: a well-defined research problem of
mechanical materials that has not been addressed by conventional
methods, or has been solved but can be outperformed by
ML-based approaches.
Possible material database
small set of dataset
~10% dataset: evaluation
~90% dataset: training data
Data order: shuffled
laser cutting machine
[6] Machine Learning-Evolutionary Algorithm
Enabled Design for 4D-Printed Active Composite
Structures
bilayer
composite+stimuli
+strain mismatch+4D
shape changing design
3D->4D
time,3D printed parts to transform
their shapes in the 4th dimension
Problem
[forward problem]
predicting shape changes for given material or
property distributions
[inverse problem]
of finding the optimal material or property
distribution to obtain the desired shape change.
[Simulation]
accurate numerical models (or predictive
models),
incorporating the forward predictive model
into some
[optimization algorithms]//topology
Optimization
topology optimization
soft actuators
Algorithms
gradient-free optimization algorithms
Gradient-based algorithms
evolutionary algorithms
Propertyies optimmization
Active Mechanical Metamaterials
ML
4D PRINTING(Direct Ink Writing Based +appli.)
Supporting ref
4D PRINTING
Paper structure
[Matlab]
[DLP+Resin]
AM
3D PRINTING
Future of additive manufacturing: Overview of 4D and 3D printed smart and
advanced materials and their applications
4D PRINTING
Topology optimization is an iterative
gradient-based design process which
minimizes an objective and satisfies a set
of selected design constraints by
distributing material in a design domain.
Define
Define
Methods
Appli.
designing certain shape-changing
responses of active composites/// other
engineering structural problems.
Mechanics-based design strategies for 4D
printing: A review
Polyjet tech
Compatible materials Tactile, opaque, flexible, transparent or rigid–
the J55™ Prime offers a wide range of materials to suit all your design needs. Multi-material capabilities let
you load up to five materials at once and create multi-color or multi shore level parts in one print. With
expansive options for color and texture combinations, there’s no need for hand painting.
convolutional neural network (CNN)
ML
relies on numerous
iterations of FE simulations to explore a
large design space,
thus suffering from high computational
cost.
Pro/Cons
because the CNN model cannot predict some complicated
designs very well whilst the inverse design problem requires
high prediction accuracy
Application
Soft Actuator
A Review of 3D-Printable Soft Pneumatic Actuators and
Sensors: Research Challenges and Opportunities
A Review of 3D-Printable Soft Pneumatic Actuators and
Sensors: Research Challenges and Opportunities
thermo-mechanical tester for compression/tesions, torsion, bending analysis
testers for measuring thermal conductivity, electrical properties, piezoelectric and pyroelectric coefficients
Tester
ANSYS
COMSOL
Abaqus
AM
Processing
[8] Combining advanced 3D printing technologies with origami principles: A new paradigm
for the design of functional, durable, and scalable springs
Anisotropic compression behaviors of bio-inspired modified body-centered cubic lattices validated by additive manufacturing
Interesting Topic
SoftPneuActuator+PDMS
Soft Pneu Actuator
PDMS/Polymer
ML
Tentative Idea
FEA
Polymer Hysteresis
Soft Pneumatic Actuator-Actuation
Constrain
AM
Modeling Analysis
Compliant mechanism
Flexible-based structure
FEA
compliant structure's kinematics and
statics
pseudo-rigid-body (PRB) model
FEA
Material
Analysis methods
FEA
FEA Tutorial for Soft Actuator
[1]
[2]
Data-driven foward+inverse
ML model
Data Acquisition
Data preprocess
inverse
forward
Strain mismatch
Compliant mechanism+flexible materials
Compliant mechanism+flexible materials
[Paper] Introducing Mass Parameters to Pseudo-Rigid-Body Models for Precisely
Predicting Dynamics of Compliant Mechanisms
Analysis methods
FEA
Dynamics
[Paper] Programmable Multistable
Perforated Shellular
Design
Material
Property
Changing Stiffness?
variable stiffness beam concepts as
stiffness change unit. Multiple units can be
combined to construct variable stiffness
Design synthesis of new compliant
mechanisms will be conducted based on
the modular unit concepts.
[Paper] Machine learning-based design and optimization of curved beams for
multistable structures and metamaterials
different flexible material
[Paper]Soft Pneumatic Actuator with Adjustable Stiffness Layers for Multi-DoF Actuation
Inspiration
[Paper]3D Printing of a Polydimethylsiloxane/
Polytetrafluoroethylene
Composite Elastomer and its Application in a Triboelectric
Nanogenerator
Reference
flexible shape changing soft pneumatic
actuator
Intro.
[Paper]Soft Robotics: A Review of Recent Developments of Pneumatic Soft Actuators
[paper] Inverse Design of Inflatable Soft Membranes Through
Machine Learning
frame constrain structure
Posture assessment
Forward+inverse
PDMS
[paper] 3D Printing of a Polydimethylsiloxane/Polytetrafluoroethylene
Composite Elastomer and its Application in a Triboelectric
Nanogenerator
Soft Pneumatic Actuator
[paper] A Review of 3D-Printable Soft
Pneumatic Actuators and
Sensors: Research Challenges and
Opportunities
Multi-DoF Actuation
[paper] Soft Pneumatic Actuator with
Adjustable Stiffness Layers for
Multi-DoF Actuation
Predicting Dynamics of Compliant
Mechanisms
[Paper] Introducing Mass Parameters to
Pseudo-Rigid-Body Models for Precisely
Predicting Dynamics of Compliant
Mechanisms
[paper] A proposed soft pneumatic
actuator
control based on angle estimation
from data-driven model
Design of soft multi-material pneumatic
actuators based on principal
strain field
[paper] Position Control for Soft Actuators,
Next Steps toward
Inherently Safe Interaction
Mixting ratio
[paper] Mechanical Characterization of
PDMS with Different Mixing
Ratios
Subtopic
[paper] Fabrication and Dynamic Modeling
of Bidirectional
Bending Soft Actuator Integrated with
Optical
Waveguide Curvature Sensor
Modelling Large Deflection of a Compliant
Mechanism
[paper] Modelling Large Deflection of a Compliant Mechanism: A Comparative
Study Using Discrete Euler Beam Constraint Model, Discrete Timoshenko Beam
Constrain Model, Finite Element Method and Experiment
[12]
Knowledge extraction and transfer in data-driven fracture mechanics
Our learning framework has the potential to shape future fusion research and tokamak development. Underspecified objectives can find
configurations that maximize a desired performance objective or even maximize power production. Our architecture can be rapidly deployed on
a new tokamak without the need to design and commission the complex system of controllers deployed today, and evaluate proposed designs
before they are constructed. More broadly, our approach may enable the discovery of new reactor designs by jointly optimizing the plasma
shape, sensing, actuation, wall design, heat load and magnetic controller to maximize overall performance.
Dimensionality reduction
Principal Component Analysis (PCA),
Linear Discriminant Analysis (LDA) and
Truncated Singular Value Decomposition
(SVD)
Factor Analysis (FA)
From Hamid Akbarzadeh, Dr.
piezo (mechanical + electrical coupling)
[09:24] Hamid Akbarzadeh, Dr.
pyroelectric (temperature + electrical
coupling)
soft material most. If not , used as
sensors/actuater
DIW directed writing
Selective laser sintering (SLS)
STRUCTURAL DESIGN
MULTI-FUNCTIONAL/Multi-Functionality
MATERIAL PROPERTY
MECHANICAL PROPERTY
Rational design of piezoelectric metamaterials with tailored electro-momentum coupling
Analysis and Optimisation of Periodic Piezoelectric Materials
Optimization of piezoelectric
metamaterials
# Multi-objective structural optimisation of
piezoelectric materials
Piezoelectric Materials
Softrobo+Motion control+dielectric
elastomer actuators
Motion Control of a Soft Circular Crawling
Robot via Iterative
Learning Control∗
As an actuation technology of soft robots,
dielectric elastomer actuators (DEAs)
exhibit many fantastic
attributes such as large strain and high energy density.
Ferroelectricity+AM
A 3D-printed molecular ferroelectric
metamaterial
Ferro
Ferroelectricity/https://www.britannica.com/science/ferroelectricity
What is the difference between dielectric and ferroelectric?
https://www.researchgate.net/post/What_are_the_differences_between_insulator_dielectrics_and_paraelectrics
ferroelectric and piezoelectric(direct piezoelectric effect/inverse piezoelectric effect)?
Piezoelectricity is a property of certain dielectric materials to physically deform in the presence of an electric
field, or conversely, to produce an electrical charge when mechanically deformed.
ferroelectricity, property of certain nonconducting crystals, or dielectrics, that exhibit spontaneous electric
polarization (separation of the centre of positive and negative electric charge, making one side of the crystal
positive and the opposite side negative) that can be reversed in direction by the application of an
appropriate electric field.
https://www.nrel.gov/materials-science/piezoelectric-ferroelectric-materials.html
Ferro Intro
Auxetic material
Auxetic materials, structures, fabrics (or
also “Auxetics”, a term that commonly
groups all of them) are materials that
exhibit an unexpected behaviour when
they are subjected to mechanical stresses
and strains.
intro.
paper
ferroelectric metamaterials
Tunable ferroelectric auxetic
metamaterials for guiding elastic waves in
three-dimensions
Metamaterials are artificial material systems that can be designed for extraordinary static and dynamic
properties, such as negative effective Poisson’s ratio, mass density, or Young’s modulus [1], [2].
Metamaterials have been proposed for numerous applications in controlling sound, vibrations, and heat.
Such applications range from wave guiding, cloaking, thermal diodes, energy transfer optimization to
acoustic rectifiers [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17]. Traditionally,
metamaterials designs are fixed, i.e., once fabricated, their effective properties cannot be changed.
However, a growing trend in metamaterials’ research is utilizing dynamically tunable designs, thus
opening the door for more potential applications and functional integration in devices. Tunability can be
achieved through a variety of methods including mechanical (e.g., by considering application of external
loads) [18], [19], [20], [21], thermal (e.g., through shape memory effects [22]), electrical (e.g., from nano
[23] to macro-scale systems [24], [25], [26]), or magnetic [27], [28], [29] stimuli. While some studies of
tunable piezoelectric metamaterials have been reported in the literature [30], [31], [32], [33], [34], [35],
harnessing the effects of ferroelectric poling to tune metamaterials properties remains relatively
unexplored. Here, we discuss the interplay between different tuning avenues in a three-dimensional
metamaterial, namely poling effects and mechanical deformations.
Ferroelectric metamaterials
PROPERTY ADJUSTABLE
stiffness designable metamaterials
negative Poisson’s ratio metamaterials
negative thermal
expansion (NTE) metamaterials
negative stiffness
energy absorption
SHAPE CHANGE/shape morphing
extreme mechanical properties
TUNABLE/PROGRAMMABLE
Twisting for soft intelligent autonomous
robot in unstructured environments
Environment-responsive soft robots
constructed from twisted LCE ribbons with
a stra
DATA-DRIVEN
REVIEW: ML
Utilization
data collection, generation and
preprocessing
mechanical property prediction
materials design
Active learning in materials science
Dielectrics
Electromagnetic Reconfiguration Using
Stretchable Mechanical Metamaterials
Papers
Tunable thermally bistable multi-material structure
Papers
3D Printed Graphene-Based
Metamaterials: Guesting Multi-
Functionality in One Gain
Advanced functional materials with fascinating properties and extended structural design have
greatly broadened their applications. Metamaterials, exhibiting unprecedented physical
properties (mechanical, electromagnetic, acoustic, etc.), are considered frontiers of physics,
material science, and engineering. With the emerging 3D printing technology, the
manufacturing of metamaterials becomes much more convenient. Graphene, due to its
superior properties such as large surface area, superior electrical/thermal conductivity, and
outstanding mechanical properties, shows promising applications to add multi-functionality into
existing metamaterials for various applications. In this review, the aim is to outline the latest
developments and applications of 3D printed graphene-based metamaterials. The structure
design of different types of metamaterials and the fabrication strategies for 3D printed
graphene-based materials are first reviewed. Then the representative explorations of 3D
printed graphene-based metamaterials and multi-functionality that can be introduced with such
a combination are further discussed. Subsequently, challenges and opportunities are provided,
seeking to point out future directions of 3D printed graphene-based metamaterials.
Stretchable
DESIGN METHODS
FORWARD DESIGN
Multidimentional
RECONFIGURATION(configuration/
configurable)
3D Printed Fractal Metamaterials with
Tunable Mechanical Properties and
Shape Reconfiguration
Electromagnetic
negative
zero
Multi-material topology optimization and additive manufacturing for metamaterials incorporating
double negative indexes of Poisson’s ratio and thermal expansion
[paper] Machine learning-based inverse design of auxetic metamaterial with zero
Poisson’s ratio
Conformal
Conformal elasticity of mechanism-based metamaterials
Nonlinear
Inverse Design of Mechanical Metamaterials with Target Nonlinear Response via a Neural Accelerated Evolution Strategy
Learning the nonlinear dynamics of mechanical metamaterials with graph
networks
the unique nonlinear dynamics of certain types of soft mechanical
metamaterials. However, capturing the nonlinear dynamic response of these
materials especially those with complex geometries, can be a challenge due
to the strong nonlinearity and large computational cost. An efficient and
reliable framework to predict the overall response of the metamaterials
based on the geometry of their building blocks is not only key to
understanding the unique behavior of metamaterials, but also vital to the
rational design of such materials.
metamaterial graph network
lattice-like metamaterial structure. The
Topological invariant and anomalous edge
modes of strongly nonlinear systems
THERMAL
Papers
Soft Robotics in Healthcare: Challenges in Design and Control
Bistable and Multistable Actuators for Soft Robots: Structures, Materials, and Functionalities
MULTISTABLE/BISTABLE
Bistable and Multistable Actuators for Soft Robots: Structures, Materials, and Functionalities
[Paper] Inverse Design of Mechanical
Metamaterials That
Undergo Buckling
AM METHODS
AM ANALYSYS
defect influence
identify the most important defect and
design features that determine the
mechanical properties of the overall
structure.
Machine learning assisted
investigation of defect influence on
the mechanical properties of
additively manufactured architected
materials
FEA
data-driven simulation
Magneto-Thermomechanically
Reprogrammable Mechanical
Metamaterials
Magneto-Thermomechanically
Reprogrammable Mechanical
Metamaterials
Magnetorheological Fluid-Based Flow
Control for Soft Robots
Video
actuation methods
such as shape-memoryalloys,
[7,8]dielectric elastomers,
[9]ionicpolymers,[10,11]and hydrogel-
based actua-tors,[1
Refer
[18]Soft Poly-Limbs: Toward a New
Paradigm of Mobile
Manipulation for Daily Living Tasks
Subtopic
shape memory polymers
(SMPs)
shape-memoryalloys
shape memory material
FABRICATION
COMPOSITE
shape memory polymers
(SMPs)
liquid crystal elastomers (LCEs)
hydrogels
composites
Applica.
Difficulties
active material involves higher
geometric or material nonlinearities (e.g., multiphysics driven
material nonlinearity).
shape-memoryalloys
intro.
depend on the spatial distributions of
materials or properties
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